Satellite signal based positioning technologies, which are mainly used outdoors, are usually not suited to deliver a satisfactory performance when used for indoor positioning, since satellite signals of global navigation satellite systems (GNSS), like the global positioning system (GPS), do not penetrate through walls and roofs strongly enough for an adequate signal reception indoors. Thus, these positioning technologies are not able to deliver a performance indoors that would enable seamless, equal and accurate navigation experience outdoors and indoors.
Therefore, several dedicated solutions for indoor positioning have been developed and commercially deployed during the past years. Examples comprise solutions that are based on pseudolites, which are ground based GPS-like short-range beacons, ultra-sound positioning solutions, Bluetooth based positioning solutions, cellular network based positioning solutions and wireless local area network (WLAN) based positioning solutions.
As an example, a positioning solution based on WLAN (as an example of a communication network) may be divided in two stages, a training stage and a positioning stage.
In the training stage, learning data is collected. The data may be collected in the form of fingerprints that are based on measurements by mobile devices. A fingerprint may contain a location estimate and measurements taken from the radio interface. The location estimate may be for example GNSS based, sensor-based, or manually inputted. Measurements taken from the radio interface may comprise, by way of example, measured radio signal strengths (RSS) and an identification of WLAN access points transmitting the radio signals. The training may be a continuous background process, in which mobile devices of a large number of consumers are continuously reporting measured data to a server. Consumers may consent to a participation in such a data collection, if their device is equipped with the needed functionality. This approach is also referred to as crowd-sourcing. Alternatively or in addition, mobile devices may be used for collecting fingerprints in a systematic manner. Collected fingerprint data may be uploaded to a database in a server or in the cloud, where algorithms may be run to generate radio models of WLAN access points and/or radio maps for positioning purposes.
In the positioning stage, a mobile device may estimate its current location based on own measurements taken from the radio interface and on the data or a subset of data that is available from the training stage. Model data or radio map data that has been generated in the training stage may be transferred to mobile devices by a server via the Internet as assistance data for use in position determinations. Alternatively, model data and/or radio map data may be stored in a positioning server to which the mobile devices may connect to via the Internet for obtaining a position estimate.
A similar approach could be used for a positioning that is based on other types of terrestrial transmitters or on a combination of different types of terrestrial transmitters.
However, these indoor solutions require either deployment of totally new infrastructure (beacons, tags and so on) or manual exhaustive radio-surveying of the buildings including all the floors, spaces and rooms. This is rather expensive and will take a considerable amount of time to build the coverage to the commercially expected level, which in some cases narrowed the potential market segment only to very thin customer base e.g. for health care or dedicated enterprise solutions. Also, the diversity of these technologies makes it difficult to build a globally scalable indoor positioning solution, and the integration and testing will become complex if a large number of technologies is needed to be supported in the consumer mobile devices, such as smartphones.
Still, in particular during the above described training stage (e.g. during crowd-sourcing), one of the most difficult problems is the accuracy of the horizontal location estimation, since WLAN or Bluetooth based positioning may not yet be available in this stage. Generally, the only source of more-or-less accurate absolute location data remains GNSS, which is however not accurate enough, especially inside buildings or even close to them. Therefore, additional sources of horizontal location data independent of GNSS are needed in order to have reliable location data also indoors.
Often on-device tracking sensors, e.g. motion sensors, are used in addition to GNSS to improve location accuracy as well as to extend the availability of location information to the GNSS-denied areas such as indoor locations. Here, the problem arises, however, that this may still result in very inaccurate horizontal position estimates as the (motion) sensors only provide relative location data (i.e. the trajectory of the motion), and a GNSS signal is still required to get at least occasionally an estimation of an absolute position (a so called “position fix”). This is problematic, since a GNSS signal may not be available for longer periods of time, in particular indoors. An additional issue, which arises, may be that often in harsh environments position estimates based on GNSS are indicated with a low position uncertainty (i.e. good quality) although the actual position estimate has a high uncertainty (i.e. low quality). In an extreme case, for example, a GNSS position fix may be indicated to lie outdoors even though the user has already gone inside the building.